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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

He has over 11 years of experience in developing and leading data science, machine learning, and big data initiatives. Currently he is helping enterprise customers modernizing their AI/ML workloads within the cloud using industry best practices. Tanvi Singhal is a Data Scientist within AWS Professional Services.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Data Lake Architect with AWS Professional Services. She is passionate about solving customer pain points processing big data and providing long-term scalable solutions. Prior to this role, she developed products in internet, telecom, and automotive domains, and has been an AWS customer.

Scripts 97
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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

AWS Machine Learning

This ensures full isolation between the workspaces following the federated model account structure mentioned in SageMaker Studio Administration Best Practices. JuMa features Following best practice architecting on AWS, the JuMa service was designed and implemented according to the AWS Well-Architected Framework.